HBIM technologies demonstrate increased potential in managing existing built heritage, leading to improved building lifecycle engineering. However, applying HBIM protocols to Cultural Heritage, particularly museum assets, represents an outstanding question. The study provides the findings of a research project conducted at the Galleria Borghese Museum to implement a geometric and informative BIM-based digital environment for museum management, as well as preventive and predictive maintenance. Furthermore, the study examines—referring to the debate over the widespread application of integrated digital technologies to cultural heritage management—the opportunities and challenges associated with digitization processes towards the implementation of Digital Twin (DT) (Vuoto in Int. J. Arch. Herit. 18(11), 1762–1795, 2024) and Digital Cultural Objects (DCO), as well as the transferability of the study’s findings. The Galleria Borghese Museum provides scholars with the opportunity to examine architecture and artworks integrated into spaces of both permanent and temporary exhibitions, multidisciplinary study areas, restoration spaces for art and architecture, and environments conceived for the valorization, communication, and participation of a large public of experts and non-experts.
3D Heritage Data Fruition and Management. Point Cloud Processing for Thematic Interpretation
Technologies and digital tools such as laser scanning and photogrammetry are nowadays widely used in the field of architectural heritage survey, being able of producing 3D models characterized by high metric and morphological accuracy. These databases are becoming essential also for the development of more effective interventions on heritage buildings. Despite the advancement of increasingly automated analytical procedures, the management and analysis of point cloud models can still be quite time-consuming and complex, depending on specific assessments to be carried out. In the direction of optimizing these processing steps, several research is being carried out by applying Artificial Intelligence processes to make predictions based on sample data. The aim of the paper is to analyse point clouds processing focusing on geometric and radiometric features for diagnostic analysis. A specific focus aims at analysing possible in-depth uses of the intensity value as a benchmark for historical surfaces assessment, toward an optimized models’ interpretation and classification of the 3D data points, integrating data and information from different sensors. Point clouds under analysis have been carried out by different acquisition techniques; this provides an interesting opportunity to compare the results in terms of intensity value produced by different sensors. The paper will analyse the State of the Art, also illustrating a set of outcomes obtained by the authors, deepening two specific case studies, in order to outline not only the main background and shortcomings in managing complex database, but also possible innovations pointing out new research questions.